U.S. patent application number 13/409890 was filed with the patent office on 2013-09-05 for method, system and computer program product for aggregating population data.
The applicant listed for this patent is Shahram Ebadollahi, Jianying HU, Robert K. Sorrentino, Jimeng Sun. Invention is credited to Shahram Ebadollahi, Jianying HU, Robert K. Sorrentino, Jimeng Sun.
Application Number | 20130231953 13/409890 |
Document ID | / |
Family ID | 49043355 |
Filed Date | 2013-09-05 |
United States Patent
Application |
20130231953 |
Kind Code |
A1 |
Ebadollahi; Shahram ; et
al. |
September 5, 2013 |
METHOD, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR AGGREGATING
POPULATION DATA
Abstract
A system, method and program product for matching members of a
population, e.g., patients, based on member similarities. Patients
are mapped to a bipartite graph with patient nodes connected by
weighted edges to clustered factor nodes, are clustered
categorically. As a new patient query is received, a similarity
measure for each other patient is generated for each cluster by
comparing cluster edges. The cluster similarity measures are
aggregated for each patient to provide a global closeness measure
to every other patient. Based on the global closeness measure, a
list of the closest patients is displayed and measurement feedback
may be provided.
Inventors: |
Ebadollahi; Shahram; (White
Plains, NY) ; HU; Jianying; (Bronx, NY) ; Sun;
Jimeng; (White Plains, NY) ; Sorrentino; Robert
K.; (Briarcliff Manor, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ebadollahi; Shahram
HU; Jianying
Sun; Jimeng
Sorrentino; Robert K. |
White Plains
Bronx
White Plains
Briarcliff Manor |
NY
NY
NY
NY |
US
US
US
US |
|
|
Family ID: |
49043355 |
Appl. No.: |
13/409890 |
Filed: |
March 1, 2012 |
Current U.S.
Class: |
705/3 ;
705/7.29 |
Current CPC
Class: |
G16H 50/70 20180101 |
Class at
Publication: |
705/3 ;
705/7.29 |
International
Class: |
G06Q 50/24 20120101
G06Q050/24; G06Q 30/02 20120101 G06Q030/02 |
Claims
1. A system for ordering members of a population, said system
comprising: a similarity measurement module listing members of a
population responsive to comparison of member features; a
similarity match module selectively presenting a number of members
as the closest matches to one member; and a feedback module
receiving feedback about the presented closest matches.
2. A system as in claim 1, wherein said similarity measurement
module graphically maps the relationship between each member and
each feature, and said similarity measurement module weights the
mapped relationship.
3. A system as in claim 2, wherein said plurality of features are
clustered and said similarity measurement module determines for
each other member a similarity measure for each cluster for said
one member.
4. A system as in claim 3, wherein said similarity measurement
module determines a global similarity measure between said one
member and said each other member, said global similarity measure
being the aggregation of cluster similarity measures for, and
indicating the closeness to, said each other member, said
similarity measurement module selectively storing a list of matches
and corresponding global similarity measures.
5. A system as in claim 4, wherein said similarity list of matches
includes a second number of members with corresponding global
similarity measures closest to said one member.
6. A system as in claim 4, wherein said similarity match module
selects and presents said number of other members having said
closest matches from stored said global similarity measures, said
weights being adjusted responsive to said feedback.
7. A system as in claim 1 further comprising: a feature data store
storing a plurality of features of said given population; and a
population store storing a list of said population members.
8. A system as in claim 7, wherein said population members are
medical patients and said features comprise diagnosis, procedure
and drug data for said medical patients.
9. A system as in claim 1, wherein said system further comprises: a
display listing said closest matches; and a graphical user
interface (GUI) displayed on said display, said feedback module
interactively receiving said feedback through said GUI.
10. A method of identifying similar members of a population, said
method comprising: receiving a query from an individual, said query
identifying a new member of a population; mapping said new member
to a bipartite graph, said bipartite graph including population
member nodes connected to factor nodes, said factor nodes being
clustered categorically; providing a global measure of closeness
for said each other member to said new member; selecting for
display a plurality of closest other members as being closest
matches; and receiving feedback regarding closeness of the selected
members responsive to said display.
11. A method as in claim 10, wherein said population members are
medical patients, said factor nodes indicating diagnosis, procedure
and drug data for said medical patients, providing a global measure
comprises a random walk, and a medical professional is making said
query and providing said feedback.
12. A method as in claim 10, further comprising weighting edges
connecting population member nodes to factor nodes in said
bipartite graph.
13. A method as in claim 12, wherein providing a global measure
comprises: comparing connections in each cluster for said new
member with connections of each other member to determine a
similarity score, s.sub.1, s.sub.2, . . . , s.sub.n, for said new
member x with each other member y; and aggregating comparison
results for said each other member, aggregated results providing a
global measure of closeness to said new member.
14. A method as in claim 13, wherein aggregating comparison results
comprises combining similarity scores for said each other member y
to provide a global similarity S.sub.x,y for each, and selectively
storing global similarities for every said other member.
15. (canceled)
16. A computer program product for identifying similar patients,
said computer program product comprising a computer usable medium
having computer readable program code stored thereon, said computer
readable program code comprising: computer readable program code
means for listing existing patients; computer readable program code
means for clustering a plurality of features of said existing
patients by category; computer readable program code means for
graphically mapping the relationship between each existing patient
and each feature; computer readable program code means for
receiving a query for a new patient; computer readable program code
means for determining a similarity measure indicating similarity
between said new patient and each existing patient for each
cluster, and listing existing patients members according to
similarity; computer readable program code means for selectively
presenting a number of existing patients as closest to said new
patient; and computer readable program code means for receiving
feedback about the presented closest patients.
17. A computer program product as in claim 16, wherein said
features comprise diagnosis, procedure and drug data for said
existing patients.
18. A computer program product as in claim 16, wherein said
computer readable program code means for determining comprises
computer readable program code means for weighting each similarity
measure, and aggregating the weighted similarity measures for said
each existing patients, said weights being adjusted responsive to
said feedback.
19. A computer program product as in claim 18, wherein said
computer readable program code means for determining comprises
computer readable program code means for listing a selected number
of said existing patients having aggregate measures indicating
those patients being closest to said new patient.
20. A computer program product as in claim 18, wherein said
computer readable program code means for selectively presenting
comprises computer readable program code means for selecting and
listing a number of said existing patients having similarity
measures indicating closest similarity to said new patient.
21. A computer program product for identifying patients similar to
a new patient, said computer program product comprising a computer
usable medium having computer readable program code stored thereon,
said computer readable program code causing a computer executing
said code to: receive query identifying a new patient; map said new
patient to a bipartite graph, said bipartite graph including
patient nodes connected to factor nodes, said factor nodes being
clustered categorically, connections being represented as weighted
edges; compare in each cluster connections between said new patient
and said factor nodes against connections for other patients;
aggregate comparison results for said each other patient,
aggregated results providing a global measure of closeness to said
new patient; select for display a plurality of closest other
patients as being closest matches; and receive feedback regarding
closeness of the selected members responsive to said display.
22. A computer program product for routing travel as in claim 21,
wherein said factor nodes indicating diagnosis, procedure and drug
data for said patients, and a medical professional is making said
query and providing said feedback.
23. A computer program product for routing travel as in claim 22,
wherein comparing cluster connections comprises determining a
similarity score, s.sub.1, s.sub.2, . . . , s.sub.n, for said new
member x with each other member y.
24. A computer program product for routing travel as in claim 23,
wherein aggregating comparison results comprises combining
similarity scores for said each other member y to provide a global
similarity S.sub.{x,y} for each, and selectively storing global
similarities for every said other member.
25. (canceled)
26. A method of identifying similar members of a population, said
method comprising: receiving a query from an individual, said query
identifying a new member of a population; mapping said new member
to a bipartite graph, said bipartite graph including population
member nodes connected to factor nodes, said factor nodes being
clustered categorically; weighting edges connecting population
member nodes to said factor nodes in said bipartite graph;
providing a global measure of closeness for said each other member
to said new member, providing said global measure comprising:
comparing connections in each cluster for said new member with
connections of each other member to determine a similarity score,
s.sub.1, s.sub.2, . . . , s.sub.n, for said new member x with each
other member y, and aggregating comparison results for said each
other member, aggregated results providing a global measure of
closeness to said new member, wherein aggregating comparison
results comprises combining similarity scores for said each other
member y to provide a global similarity S.sub.x,y for each, and
selectively storing global similarities for every said other
member, and wherein S.sub.{x}=t.sub.1*s.sub.1+t.sub.2*s.sub.2+ . .
. +w.sub.n*s.sub.n, where t.sub.1 . . . t.sub.n are the weighting
coefficient on the factors, s.sub.i is the match result of x and y
on factor i, and i is between 1 and n; selecting for display a
plurality of closest other members as being closest matches; and
receiving feedback regarding closeness of the selected members
responsive to said display, wherein said weighting coefficients are
adjusted responsive to said feedback.
27. A computer program product for identifying patients similar to
a new patient, said computer program product comprising a computer
usable medium having computer readable program code stored thereon,
said computer readable program code causing a computer executing
said code to: receive query identifying a new patient from a
medical professional; map said new patient to a bipartite graph,
said bipartite graph including patient nodes connected to factor
nodes, said factor nodes being clustered categorically and
indicating diagnosis, procedure and drug data for said patients,
connections being represented as weighted edges; compare in each
cluster connections between said new patient and said factor nodes
against connections for other patients, a similarity score,
s.sub.1, s.sub.2, . . . , s.sub.n being determined for said new
member x with each other member y; aggregate comparison results for
said each other patient, aggregated results providing a global
measure of closeness to said new patient, similarity scores being
combined for said each other member y to provide a global
similarity S.sub.{x,y} for each, and global similarities being
selectively stored for every said other member, wherein
S.sub.{x}=t.sub.1*s.sub.1+t.sub.2*s.sub.2+ . . . +w.sub.n*s.sub.n,
where t.sub.1 . . . t.sub.n are the weighting coefficient on the
factors, s.sub.i is the match result of x and y on factor i, and i
is between 1 and n; select for display a plurality of closest other
patients as being closest matches; and receive feedback from said
medical professional regarding closeness of the selected members
responsive to said display, wherein said weighting coefficients
being adjusted responsive to said feedback.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention is related to aggregating population
data according to member similarity and more particularly to
aggregating electronic health records from multiple data sources
based on patient similarities.
[0003] 2. Background Description
[0004] Healthcare digitization has produced voluminous data.
Doctor's offices, that have been converting paper patient records
to electronic records, collect new patient data in an electronic
format, e.g., as electronic health records (EHR). EHRs make patient
histories readily available, e.g., for making/supporting clinical
decisions. Existing EHR data can facilitate subsequent patient
diagnosis and treatment. Matching new patient symptoms and other
characteristics to patient histories to find patients with similar
symptoms and characteristics, may provide the patient's doctor with
an early diagnosis and suggest treatment. At the very least, it
will winnow the potential diagnosis and treatment to a few likely
diagnoses and treatments. However, while multiple patients may have
the same diagnosis, no two people are identical, e.g., symptoms and
treatment may be different. Thus typically, complete matches are
infrequent.
[0005] While finding complete matches in the voluminous,
multi-dimensional data may be a relatively simple task, defining
and finding similar cases can be much more complicated. The degree
of similarity desired, for example, can complicate matching similar
patient histories. Further, having been collected by multiple
health care providers in different formats, the raw history data
may be in multiple locations in different databases/sources in
multiple incompatible formats. The data formats may include, for
example, International Classification of Diseases, Ninth Revision
(ICD9), Current Procedural Terminology (CPT) codes, National Drug
Codes (NDC), LAB, clinical notes. These formats rely heavily on
coding the data both to quickly categorize it and for efficient
data handling.
[0006] However, the variety and variation of these codes can
complicate comparing data further. Typically there isn't a one to
one mapping for codes, making it more difficult to: value the
relevance of the raw data, determine event timeliness, and
determine for each match what coded events are more important than
others. Missing data or mismatched codes may mask similarities.
Noise, e.g., unrelated symptoms, in the raw data can further shade
results. Moreover, once similar results are matched, those results
are not an ultimate determination. That, typically, is made by a
requesting physician. Currently, there is no mechanism that allows
the requesting physician to provide similarity goodness feedback
based on his/her clinical intuition used to make a final diagnosis
and prescribe an appropriate treatment.
[0007] Thus, there is a need for a way to identify similarities in
patient histories and aggregate the results to reflect a global
similarity.
SUMMARY OF THE INVENTION
[0008] A feature of the invention is a similarity measure for
grouping members of a population based on member similarities;
[0009] Another feature of the invention is improved matching of
medical patients with similar conditions based on patient
similarities;
[0010] Another feature of the invention is improving matching of
medical patients with similar conditions based on feedback from
medical professionals with regard to previous grouping;
[0011] Yet another feature of the invention is a similarity measure
for matching medical patients based on patient similarities, and
further honed by feedback from medical professionals with regard to
previous grouping.
[0012] The present invention relates to a system, method and
program product for matching members of a population, e.g.,
patients, based on member similarities. Patients are mapped to a
bipartite graph with patient nodes connected by weighted edges to
clustered factor nodes, are clustered categorically. As a new
patient query is received, a similarity measure for each other
patient is generated for each cluster by comparing cluster edges.
The cluster similarity measures are aggregated for each patient to
provide a global closeness measure to every other patient. Based on
the global closeness measure, a list of the closest patients is
displayed and measurement feedback may be provided.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0014] FIG. 1 shows an example of a system for matching patients to
other patients based on patient similarities according to a
preferred embodiment of the present invention;
[0015] FIG. 2 shows an example of matching a patient to existing
patients according to a preferred embodiment of the present
invention;
[0016] FIG. 3 shows an example of the similarity measurement module
graphically modeling patient data as patient nodes connected by
edges to factor nodes, grouped or clustered.
DESCRIPTION OF PREFERRED EMBODIMENTS
[0017] As will be appreciated by one skilled in the art, aspects of
the present invention may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0018] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0019] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0020] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0021] Computer program code for carrying out operations for
aspects of the present invention may be written in any combination
of one or more programming languages, including an object oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0022] Aspects of the present invention are described below with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer program
instructions. These computer program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
[0023] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0024] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0025] Turning now to the drawings and, more particularly, FIG. 1
shows an example of a system 100 for matching patients to other
patients based on patient similarities according to a preferred
embodiment of the present invention. In this example, a similarity
measurement module 102, similarity match module 104 and feedback
module 106 are located, for example only, on multiple individual
computers networked together over a network 108. The individual
computers may be located at a single location or distrusted at
remote locations. Further, one, two or all of the preferred modules
102, 104, 106 may be collocated on a single computer. Although
described in terms of medical data, databases and patients, the
present invention has application to aggregating individuals, human
or otherwise, in any population of any type (e.g., a fleet of cars,
ships or aircraft) according to similarities.
[0026] The similarity measurement module 102 determines a pairwise
patient similarity score for a current patient against histories,
e.g., in storage 110, for other individual patients to identify
similar conditions. In particular, the similarity measurement
module 102 uses a general patient similarity measure for handling
heterogeneous patient records as set forth hereinbelow. The
similarity match module 104 searches resulting similarity scores
and retrieves the histories for the top-k similar scores. The top-k
similar scores are returned, e.g., displayed 112, for a medical
professional, e.g., a doctor to select one or more similar patients
and make a diagnosis for the current patient and suggest treatment.
The feedback module 106 receives general patient similarity measure
incorporating feedback from experts, e.g., the efficacy of the
treatment selected, to further customize and hone the similarity
match performed by the similarity measurement module 102.
[0027] FIG. 2 shows an example of matching a patient to existing
patients according to a preferred embodiment of the present
invention. When a preferred system (e.g., 100 in FIG. 1) receives a
query 120 about a patient, the similarity measurement module 102
models 122 patient data as a bipartite graph with two types of
nodes, patient and clustered factor nodes connected by edges. Then,
the similarity measurement module 102 determines a cluster
similarity score 124 for each other patient in each factor cluster.
The similarity measurement module 102 combines scores 126 for each
patient to provide a global similarity measure for each. The
similarity measurement module 102 stores 128 the results, which
indicate how close each other patient matches the query patient.
Optionally, only a selected number of the closest matches are
stored, e.g., based on the highest global scores for each other
patient. The similarity match module 104 searches the stored
similarity scores, retrieves the top-k similar scores and presents
130 histories for those top-k patients. The requesting medical
professional, e.g., the query patient's doctor, reviews the
results, e.g., on display 112 using a typical graphical user
interface (GUI). The requesting medical professional can review the
results and provide feedback 132 to feedback module 106 through the
GUI, which the feedback module 106 uses to re-weight the graph
edges.
[0028] So, as shown in the example of FIG. 3, the similarity
measurement module 102 models (120 in FIG. 2) patient data as a
bipartite graph with two types of nodes, patient nodes 140-1-140-m
and factor nodes, grouped or clustered in clusters 142-1-142-n,
where n=three (3) in this example. The patient nodes 140-1-140-m
correspond to individual patients. Each factor cluster 142-1-142-n
may be weighted w and is associated a particular feature, e.g.,
patient codes. The clusters 142-1-142-n can have multiple types
with each type associated with a different type weight t.sub.i.
Relationships between the patients and individual cluster nodes are
indicated by edges 144-1-144-j. Weights a, associated with each of
the edges 144-1-144-j, indicate the importance of each particular
relationship.
[0029] The similarity measurement module 102 determines 124 a
cluster similarity score, s.sub.1, s.sub.2, . . . , s.sub.n, for
each new or requesting patient x with each other patient y, i.e.,
nodes 140-1-140-m, in each factor cluster 142-1-142-n. For example,
if two patients x and y connect to a common factor f, the match
result between x and y on f is 1; and otherwise f is 0, i.e., no
match. This match result can be generalized to be weighted by
w.sub.x*w.sub.y*t where w.sub.x, w.sub.y are the edge weights from
x or y to f, and t is the type weight of f. A general example of
determining a similarity measure between members of a population
based on connection to members of another population is described
by J. Sun et al., "Neighborhood Formation and Anomaly Detection in
Bipartite Graphs," Fifth IEEE International Conference on Data
Mining, ICDM pp. 418-425, November, 2005, the contents of which are
incorporated herein by reference. Then, the similarity measurement
module 102 combines cluster scores 126 for each patient 140-1-140-m
to provide a global similarity for each,
S.sub.{x,y}=t.sub.1*s.sub.1+t.sub.2*s.sub.2+ . . .
+w.sub.n*s.sub.n, where t.sub.1 . . . t.sub.n are the weighting
coefficient on the factors, s.sub.i is the match result of x and y
on factor i, and i is between 1 to n.
[0030] In this example, the factor clusters 142-1-142-n are
categories for the individual nodes, which include a diagnosis code
cluster 142-1, e.g., Clinical Classifications Software (CCS); a
procedure code (CPT) cluster 142-2, and a drug code (NDC) cluster
142-n. Also, individual factor nodes can indicate symptoms,
indicate a temporal logical sequence modeled as factor nodes, or be
a very general (e.g., logical) indicator. For example, factor nodes
can indicate glucose level as normal, low, or high. In another
example, a factor node can indicate the logical sequence"CCS.1
follows with (CPT.2 and NDC.2)." For each cluster 142-1-142-n, the
similarity measurement module 102 determines the cluster similarity
124 of requesting patient x with existing patient y 140-1-140-m
based on the correlation of factors between the two patients x and
y. Optionally, instead of using a weighted familiarity approach to
arrive at similarity measurements, a random walk approach as also
described by Sun et al. may be used. The similarity measurement
module 102 stores 128 the global similarity measure S.sub.x,y,
e.g., in storage 110, for use by the similarity match module
104.
[0031] The similarity match module 104 searches and retrieves and
displays 130 similarity scores S.sub.x,1-S.sub.x,m for similarity
matches. Matches may be selected as the top-k similar scores, where
k is some number between 1 and m, the number of matched patients.
Further, k can be selected, for example, by default or when
requested. The similarity match module 104 retrieves and presents
130 the matching similar scores, e.g., displaying 112 the matches
for a medical professional, such as a nurse or a doctor. The
medical professional can review the displayed results, either
individually S.sub.x,1-S.sub.x,m, or the selected similarity
matches. The medical professional may further review the efficacy
of the treatment selected and/or the similarity to patient y or the
group of patients, for example, and provide feedback 132 based on
that review.
[0032] The feedback module 106 receives feedback general patient
similarity measure incorporating from experts, e.g.,
including/excluding certain data sources, varying weights for each.
So, for example, using a typical GUI, the medical professional can
select individual factor nodes or clusters for exclusion in the
similarity measure S.sub.y,z. Also, the medical professional can
adjust both edge weights and factor weights. Based on this feedback
32, the similarity measurement module 102 regenerates the global
similarity measures S.sub.x,1-S.sub.x,m for the patient x.
[0033] Thus advantageously, a preferred system 100 handles multiple
data sources, incorporating expert feedback to arrive at the best
selection of similar patients. The preferred similarity measurement
module leverages the flexibility of a preferred factor graph model
to model to selectively add/remove additional features or data
sources to the consideration. The factor graph model also enables
varying weighting coefficients on different features. Optimal
weighting coefficients may be determined using a classification
problem on all pairs of patients with experts labeling the results
positively or negatively.
[0034] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0035] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
invention has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the invention. The
embodiment was chosen and described in order to best explain the
principles of the invention and the practical application, and to
enable others of ordinary skill in the art to understand the
invention for various embodiments with various modifications as are
suited to the particular use contemplated.
* * * * *